Simplify/uniformize sample data setup in plot_types examples.

- Most 2D data examples don't need Z.min() subtraction (the
  normalization adjusts itself as needed).
- Tweak spacing in the expression for Z to be the same everywhere.
- Use shorter .min(), .max() methods instead of free functions.

Author: anntzer

plot_types/arrays/contourf.py

@@ -13,8 +13,7 @@
 # make data
 X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
 Z = (1 - X/2. + X**5 + Y**3) * np.exp(-X**2 - Y**2)
-Z = Z - Z.min()
-levels = np.linspace(np.min(Z), np.max(Z), 7)
+levels = np.linspace(Z.min(), Z.max(), 7)
 
 # plot
 fig, ax = plt.subplots()

plot_types/arrays/imshow.py

@@ -14,7 +14,6 @@
 # make data
 X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
 Z = (1 - X/2. + X**5 + Y**3) * np.exp(-X**2 - Y**2)
-Z = Z - Z.min()
 Z = Z[::16, ::16]
 
 # plot

plot_types/arrays/pcolormesh.py

@@ -15,7 +15,6 @@
 # make full-res data
 X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
 Z = (1 - X/2. + X**5 + Y**3) * np.exp(-X**2 - Y**2)
-Z = Z - Z.min()
 
 # sample unevenly in x:
 dx = np.sqrt((np.arange(16) - 8)**2) + 6
@@ -29,6 +28,6 @@
 fig, ax = plt.subplots()
 ax.grid(False)
 
-ax.pcolormesh(X, Y, Z, vmin=0, vmax=1.5)
+ax.pcolormesh(X, Y, Z, vmin=-0.5, vmax=1.0)
 
 plt.show()

plot_types/unstructured/tricontour.py

@@ -13,7 +13,6 @@
 # make structured data
 X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
 Z = (1 - X/2. + X**5 + Y**3) * np.exp(-X**2 - Y**2)
-Z = Z - Z.min()
 
 # sample it to make unstructured x, y, z
 np.random.seed(1)
@@ -27,7 +26,7 @@
 fig, ax = plt.subplots()
 
 ax.plot(x, y, '.k', alpha=0.5)
-levels = np.linspace(np.min(Z), np.max(Z), 7)
+levels = np.linspace(Z.min(), Z.max(), 7)
 ax.tricontourf(x, y, z, levels=levels)
 
 ax.set(xlim=(-3, 3), ylim=(-3, 3))

plot_types/unstructured/tripcolor.py

@@ -12,8 +12,7 @@
 
 # make structured data
 X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
-Z = (1 - X/2. + X**5 + Y**3)*np.exp(-X**2-Y**2)
-Z = Z - Z.min()
+Z = (1 - X/2. + X**5 + Y**3) * np.exp(-X**2 - Y**2)
 
 # sample it to make unstructured x, y, z
 np.random.seed(1)